Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
Objectives
Methods
Mechanical loading of joints with osteoarthritis (OA) results in pain-related functional impairment, altered joint mechanics and physiological nociceptor interactions leading to an experience of pain. However, the current tools to measure this are largely patient reported subjective impressions of a nociceptive impact. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this study, we gathered preliminary data on healthy volunteers to analyse whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus. Following ethical approval, 15 healthy volunteers completed 5 movement and stationary activities in 2 conditions; a control setting and then repeated with an applied quantified thermal pain stimulus to their right knee. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes for skin temperature and conductivity and photoplethysmography (PPG) sensors were manually timestamped to the integrated system. Pilot data showed EMG, GRF and IMU fluctuations within 0.5 seconds of each other in response to a thermal trigger. Preliminary analysis on the 15 participants tested has shown skin conductance, PPG, EMG, GRFs, joint angles and kinematics with varying increases and fluctuations during the thermal condition in comparison to the control condition. Preliminary results suggest physiological and biomechanical data outputs can be linked and identified in response to a defined nociceptive stimulus. Study data is currently founded on healthy volunteers as a proof-of-concept. Further exploratory statistical and sensor readout pattern analysis, alongside early and late-stage OA patient data collection, can provide the information for potential development of wearable nociceptive sensors to measure disease progression and treatment effectiveness.
Current tools to measure pain are broadly subjective impressions of the impact of the nociceptive impulse felt by the patient. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this proof-of-concept study, we evaluated whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus. Following ethical approval, we applied a quantified thermal pain stimulus to a volunteer during stationary standing in a gait lab setting. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes and skin thermal sensors were manually timestamp linked to the integrated system.Abstract
Objectives
Method
The aim of this study was to compare the clinical outcome, radiological outcome, activity level and functional outcome of hip resurfacing against metal on metal (MOM) hip arthroplasty. Matched pairs of patients were selected from consecutive patients who had either MOM arthroplasty (n=236) or hip resurfacing (n=264). We matched 346 patients (173 pairs) in terms of age, sex, diagnosis, and a minimum follow up of 60 months. The functional outcome was assessed using Harris, Charnley-MDP, SF-36, UCLA and Tegner scores. Mean follow up was 67 months (61–80). Mean age was 54.5 years. Femoral neck fractures were seen in 4 patients in the resurfacing group. The mean acetabular inclination was 42.8 deg and 44.3 deg in the resurfacing and MOM groups. Mean stem subsidence was 1.2mm. Bony ingrowth was seen in ninety six stems and all stems were stable by Engh s criterion. Radiolucent halo was observed around the stem of two resurfacing heads. The mean Harris hip score was 87.9 and 88.2 in the MOM and resurfacing groups respectively (p=0.76). The SF 36 score was 77.8 and 80.1 (p=0.4). The UCLA and Tegner scores were 6.1 and 3.6 for the resurfacing group and 5.9 and 3.9 for the MOM group. Nine patients in the resurfacing group had a postoperative painful limp which settled by 3 months. There was no radiological evidence of implant failure at last follow up. Survival at 5 years was 100% for the MOM group and 94.1% for the resurfacing group. Functional outcome and activity levels increased in both groups with no difference between the groups. Post operative complications were fewer in MOM group and return to activity was quicker. It appears that resurfacing arthroplasty offers no medium term advantages over MOM arthroplasty. However longer follow up is required to establish the longevity and durability of this implant.
